In modern wireless networks, scheduling and certain network parameters, such as power control parameters, require constant adaptation to maximize performance. Traditionally, simple algorithms are created in order to perform real time network optimization. For instance scheduling is often performed by measuring a signal-to-interference-plus-noise ratio (SINR) and then determining transmit parameters that achieve a certain spectral efficiency that is in line with that SINR, such as choosing a certain modulation and coding. Some more advanced systems include additional steps, such as adjusting the transmit parameters based on current error rates. Techniques like this and others to optimize network parameters are performed with these relatively simple steps. However the actual factors in determining the best network settings are much more complex and involve many other known and potentially unknown factors. Additionally these techniques rely on feedback from and between mobile devices and the base station, which is limited by both the amount and frequency of the feedback.
Accordingly, there exists a need for methods, systems, and computer program products for optimizing a predictive model for mobile network communications based on historical context information.